As AI evolves from copilots to autonomous agents, product teams face a new engineering challenge: deciding which workflows should become agentic, how much autonomy they require, and how to design systems that are reliable in production. This session explores the architectural decisions behind successful AI products from identifying high-value workflows and selecting the appropriate autonomy model to balancing human oversight, orchestration, evaluation, and operational risk. Using real-world enterprise examples, we'll examine the patterns that distinguish scalable AI systems from isolated proofs of concept, and how product and engineering teams can build agents that deliver measurable business outcomes. Key takeaways: → Identify which workflows are best suited for assistants, copilots, or autonomous agents. → Understand the architectural trade-offs between autonomy, reliability, and human oversight. → Learn how leading teams evaluate AI systems using operational and business metrics not just model performance.